Traffic accidents on urban roads are a major cause of death despite the development of traffic safety measures. However, the prediction of casualties in urban road traffic accidents has not been deeply explored in previous research. Effective forecasting methods for the casualties of traffic accidents can improve the manner of traffic accident warnings, further avoiding unnecessary loss. This paper provides a practicable model for traffic forecast problems, in which ten variables, including time characteristics, weather factors, accident types, collision characteristics, and road environment conditions, were selected as independent factors. A mixed-support vector machine (SVM) with a genetic algorithm (GA), sparrow search algorithm (SSA), grey wolf optimizer algorithm (GWO) and particle swarm optimization algorithm (PSO) separately are proposed to predict the casualties of collisions. Grounded on 4285 valid urban road traffic collisions, the computing results show that the SSA-SVM performs effectively in the casualties forecast compared with the GWO-SVM, GA-SVM and PSO-SVM.

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Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads

We use a first-principles calculation to explore the effects of vacancies on structural, electronic and optical properties of β-Si3N4 based on density functional theory (DFT). The results show that...

Effects of vacancy on the electronic and optical properties of β-Si3N4 by first-principles

A large amount of traffic safety information has been generated. This will further promote the sustainable development of transport. However, its content, form, and structure are complex and scattered, lacking effective information integration and a comprehensive framework. Combined with the concept of safety analysis, a traffic safety management knowledge graph was designed for structured data, which include 54 types of node entities and 14 types of relationship entities. Six types of information were collected and imported, including illegal acts, vehicle failure, emergency response, legal norms, organization information, and road-related information. Ultimately, a knowledge query function was realized using Cypher, and an automatic Q&A function was created based on rule matching. A traffic accident knowledge graph was constructed for unstructured data, with people and institutions involved, vehicles involved, and accidents as the core, including 21 types of node entities and 22 types of relationship entities. Comparing the node entity extraction performance of Bert, Bert-CRF, Bert-BiLSTM, and Bert-BiLSTM-CRF models, Bert BiLSTM-CRF performs the best. The Bert model was used for relationship entity extraction. The traffic accident knowledge graph can structurally display accident information and support a query function to facilitate safety analysis.

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Architecture and Application of Traffic Safety Management Knowledge Graph Based on Neo4j

The TransE model plays a key role in dealing with data sparsity and promotes the development of knowledge graphs completion. However, TransE has some difficulties in dealing with one-to-many, many-to-one, many-to-many and transmission relationships. In order to solve this problem, this paper proposes a knowledge representation learning model based on hyperplane projection and relational attributes, namely TransH-RA. First of all, we introduce the idea of hyperplane projection based on the TransE model, this idea is inspired by TransH, which makes different entities have different roles in a specific relationship, thus reducing the constraints of TransE translation rules, and map the head entity h and tail entity t to the plane of special relation r; Secondly, considering that it is not easy to identify different similar entities, the neighborhood information of entities is added to learn the neighborhood of entities around different entities; Then, in order to further strengthen the ability to deal with complex relationships, attribute features of relationships are added and attribute knowledge is embedded; Eventually, during the training of the model, the probability method is chosen to replace the head and tail entities. Link prediction experiments are conducted on the public datasets FB15K and WN18, and the triple classification experiments on the datasets WN11, FB13 and FB15K are carried out to analyze and verify the effectiveness of the proposed method. The evaluation results show that our method achieves state-of-the-art performance on MeanRank, Hits@10 and ACC indicators compared with TransE and TransH.

TransH-RA: A Learning Model of Knowledge Representation by Hyperplane Projection and Relational Attributes

The TransE model plays a key role in dealing with data sparsity and promotes the development of knowledge graphs completion. However, TransE has some difficulties in dealing with one-to-many, many-to-one, many-to-many and transmission relationships. In order to solve this problem, this paper proposes a knowledge representation learning model based on hyperplane projection and relational attributes, namely TransH-RA. First of all, we introduce the idea of hyperplane projection based on the TransE model, this idea is inspired by TransH, which makes different entities have different roles in a specific relationship, thus reducing the constraints of TransE translation rules, and map the head entity h and tail entity t to the plane of special relation r; Secondly, considering that it is not easy to identify different similar entities, the neighborhood information of entities is added to learn the neighborhood of entities around different entities; Then, in order to further strengthen the ability to deal with complex relationships, attribute features of relationships are added and attribute knowledge is embedded; Eventually, during the training of the model, the probability method is chosen to replace the head and tail entities. Link prediction experiments are conducted on the public datasets FB15K and WN18, and the triple classification experiments on the datasets WN11, FB13 and FB15K are carried out to analyze and verify the effectiveness of the proposed method. The evaluation results show that our method achieves the better results on MeanRank, Hits@10 and ACC indicators compared with TransE and TransH. 

Traffic accident data include multidimensional dynamic and static factors such as “people, vehicles, roads, and environment” at the time of the accident, which is one of the important data sources for improving the traffic safety environment. Based on the case data of traffic accidents and the construction idea of knowledge graph, the knowledge demand, knowledge modeling, knowledge extraction, and knowledge storage of traffic accidents are analyzed in detail. Finally, the traffic accident knowledge graph is constructed. The visualization analysis of accident is carried out from four angles: accident portrait, accident classification, accident statistics, and accident correlation path. The visualized graphic network displayed by the traffic accident knowledge combines human cognition with machine cognition, which improves human’s ability to understand massive and complicated data. The theoretical system of constructing traffic accident knowledge graph has certain reference significance in the follow-up research on the analysis of massive traffic accident data.

/pdf/analysis-of-traffic-accident-based-on-knowledge-graph-1ppzidbz.pdf

Analysis of Traffic Accident Based on Knowledge Graph

The energetics, electronic structures, and optical properties of several neutral vacancies for γ-Si3N4 are studied based on density function theory within the generalized gradient approximation. The binding and formation energies of nitrogen vacancy are smaller than that of silicon vacancies, implying that nitrogen vacancy can be easily formed in γ-Si3N4. Corresponding density of states of different point vacancies is analyzed. We concluded that the neutral silicon vacancies introduce the p-type carriers into the system, whereas single nitrogen vacancy leads to an n-type semiconductor. The results show indirect semiconductor of nitrogen vacancy for γ-Si3N4. The effects of optical properties are discussed on single vacancies for γ-Si3N4. For silicon vacancies, the materials have much higher static dielectric constants than these of nitrogen vacancy and perfect γ-Si3N4. The single nitrogen vacancy for γ-Si3N4 has no effects on absorption and reflection in visible and infrared light. For silicon vacancy, it is significantly increased.

Xiao-fei Hu

Papers

Analysis of Traffic Accident Based on Knowledge Graph

Effects of Single Vacancy on Electronic and Optical Properties for γ-Si3N4

Simulation study on ramp inflow for hybrid autonomous driving